derfinder2 results exploration

Introduction

This report is meant to help explore the results of the derfinder2 package. While the report is rich, it is meant to just start the exploration of the results and exemplify some of the code used to do so. You will most likely need a more in-depth analysis for your specific data set.

Code setup

suppressMessages(library("derfinder2"))
suppressMessages(library("IRanges"))
library("ggplot2")
suppressMessages(library("gridExtra"))
library("parallel")
suppressMessages(library("GenomicRanges"))
suppressMessages(library("ggbio"))
suppressMessages(library("TxDb.Hsapiens.UCSC.hg19.knownGene"))

## Set the prefix and number of cores if not present
if (!"prefix" %in% ls()) {
    prefix <- "run1"
}
if (!"cores" %in% ls()) {
    ## Make sure you asked for enough cores in the cluster queue!
    cores <- 4
}
if (!"makeBestPlots" %in% ls()) {
    makeBestPlots <- TRUE
}

Using 1 cores on prefix run1 from path /amber2/scratch/lcollado/derfinderExample/derAnalysis.

## Load files
load(paste0(prefix, "/fullRegsAnno.Rdata"))
load(paste0(prefix, "/fullNullstats.Rdata"))
load(paste0(prefix, "/fullNullwidths.Rdata"))
load(paste0(prefix, "/fullTime.Rdata"))
load(paste0(prefix, "/fullRegionsDF.Rdata"))
load(paste0(prefix, "/chr1/optionsStats-1.Rdata"))

## Get chr lengths
data(hg19Ideogram, package = "biovizBase")
seqlengths(fullRegionsDF) <- seqlengths(hg19Ideogram)[names(seqlengths(fullRegionsDF))]

## Find which chrs are present in the data set
chrs <- levels(seqnames(fullRegionsDF))
names(chrs) <- gsub("chr", "", chrs)

Processing code.

### p-value functions Recursively figure out the pvalues
getPval <- function(df, vals, res, i, stop) {
    ## Figure out where to work
    if (df$null[1] > vals[i]) {
        j <- 1
    } else {
        j <- which(head(df$null, 20) > vals[i])[1]
        if (is.na(j)) {
            j <- which(head(df$null, 100) > vals[i])[1]

            if (is.na(j)) {
                j <- which(df$null > vals[i])[1]

                if (is.na(j)) {
                  return(res)
                }
            }
        }
    }

    res[i] <- df$n[j]

    if (i == stop) {
        return(res)
    } else {
        newdf <- df[j:nrow(df), ]
        getPval(newdf, vals, res, i + 1, stop)
    }
}

## Apply the recursive function by chunks
getPval.apply <- function(k, cuts, df, vals, df.n) {
    start <- which(df$null >= cuts[k])[1]
    newvals <- as.numeric(vals[vals >= cuts[k] & vals < cuts[k + 1]])
    tmpres <- rep(0, length(newvals))
    if (is.na(start)) {
        return(tmpres)
    } else {
        end <- which(df$null <= cuts[k + 1])[1]
        if (is.na(end)) {
            end <- df.n
        } else {
            end <- min(1 + end, df.n)
        }
        newdf <- df[start:end, ]
        res <- getPval(newdf, newvals, tmpres, 1, length(tmpres))
        return(res)
    }
}

## Should it re-process the files or load previous ones?
procFiles <- sapply(c(paste0(prefix, "/fullRegsAnnoPooled.Rdata"), paste0(prefix, 
    "/fullNullSummary.Rdata")), file.exists)
procFiles <- all(procFiles)

if (procFiles) {
    ## Just load the pre-saved files
    load(paste0(prefix, "/fullRegsAnnoPooled.Rdata"))
    load(paste0(prefix, "/fullNullSummary.Rdata"))
} else {
    ## Process the nullstats
    nulls <- unlist(lapply(fullNullstats, as.numeric))
    nullstats <- Rle(sort(nulls))
    fullRegsAnno.ord <- fullRegsAnno[order(fullRegsAnno$value), ]
    vals <- Rle(fullRegsAnno.ord$value)

    ## Construct null info
    null.df <- data.frame(null = runValue(nullstats), n = length(nullstats) - 
        cumsum(runLength(nullstats)))


    ## Calculate pvalues using all null stats from all the chrs
    nCuts <- ceiling(length(vals)/1000)
    cuts <- quantile(vals, (0:nCuts)/nCuts)
    cuts[length(cuts)] <- cuts[length(cuts)] * 1.1
    df.n <- nrow(null.df)
    pvals <- mclapply(seq_len(length(cuts) - 1), getPval.apply, cuts = cuts, 
        df = null.df, vals = vals, df.n = df.n, mc.cores = cores)
    pvals <- do.call(c, pvals)
    fullRegsAnno.ord$pvaluesPool <- (pvals + 1)/(length(nullstats) + 1)
    fullRegsAnno$pvaluesPool <- fullRegsAnno.ord$pvaluesPool[order(fullRegsAnno$value)]
    rm(nullstats, fullRegsAnno.ord, vals, null.df, nCuts, cuts, df.n, pvals)

    ## For Fstat vs width
    widths <- unlist(lapply(fullNullwidths, as.numeric))
    howMany <- unlist(lapply(fullNullstats, length))
    fullNullSummary <- data.frame(nullstat = nulls, nullwidth = widths, chr = rep(paste0("chr", 
        names(fullNullstats)), howMany))
    rm(nulls, widths, howMany)
    fullNullSummary$area <- fullNullSummary$nullstat * fullNullSummary$nullwidth

    ## Area penalization scheme 1
    fullRegsAnno$penArea1 <- fullRegsAnno$value * fullRegsAnno$L
    tmp <- fullRegsAnno$L > 30
    fullRegsAnno$penArea1[tmp] <- fullRegsAnno$value[tmp] * 30 + fullRegsAnno$value[tmp] * 
        sqrt(fullRegsAnno$L[tmp] - 30)
    fullNullSummary$penArea1 <- fullNullSummary$nullstat * fullNullSummary$nullwidth
    tmp <- fullNullSummary$nullwidth > 30
    fullNullSummary$penArea1[tmp] <- fullNullSummary$nullstat[tmp] * 30 + fullNullSummary$nullstat[tmp] * 
        sqrt(fullNullSummary$nullwidth[tmp] - 30)

    ## Area penalization scheme 2
    fullRegsAnno$penArea2 <- fullRegsAnno$value * fullRegsAnno$L
    tmp <- fullRegsAnno$L > 30
    fullRegsAnno$penArea2[tmp] <- fullRegsAnno$value[tmp] * 30 + fullRegsAnno$value[tmp] * 
        log(fullRegsAnno$L[tmp] - 30)
    fullNullSummary$penArea2 <- fullNullSummary$nullstat * fullNullSummary$nullwidth
    tmp <- fullNullSummary$nullwidth > 30
    fullNullSummary$penArea2[tmp] <- fullNullSummary$nullstat[tmp] * 30 + fullNullSummary$nullstat[tmp] * 
        log(fullNullSummary$nullwidth[tmp] - 30)
    rm(tmp)
}

## For genome overview plots -- needed before version 0.0.12
## fullRegionsDF$significant <- factor(fullRegionsDF$pvalues < 0.05,
## levels=c(TRUE, FALSE)) fullRegionsDF$significantQval <-
## factor(fullRegionsDF$qvalues < 0.10, levels=c(TRUE, FALSE))
fullRegionsDF$significantPool <- factor(fullRegsAnno$pvaluesPool < 0.05, levels = c(TRUE, 
    FALSE))

P-values distributions

P-values

## Visual comparison
p1 <- ggplot(fullRegsAnno, aes(x = pvalues, colour = chr)) + geom_line(stat = "density") + 
    xlim(0, 1) + labs(title = "Density of p-values - by chr") + xlab("p-values") + 
    scale_colour_discrete(limits = chrs)
p2 <- ggplot(fullRegsAnno, aes(x = pvaluesPool, colour = chr)) + geom_line(stat = "density") + 
    xlim(0, 1) + labs(title = "Density of p-values - pooled") + xlab("p-values") + 
    scale_colour_discrete(limits = chrs)
grid.arrange(p1, p2)

plot of chunk pvals

This plot is useful for comparing the densities of the permutted p-values for the regions. The top panel shows the p-values computed by chromosome, meaning that the null statistics used were chromosome specific. The second panel shows the p-values computed by pooling the null statistics from all the chromosomes.

There are a total of 42445 regions and 8993 null statistics across all chromosomes.

## Compare the pvalues
summary(fullRegsAnno$pvalues)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0011  0.0564  0.2810  0.3530  0.6060  1.0000
summary(fullRegsAnno$pvaluesPool)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0001  0.0001  0.0001  0.0019  0.0001  1.0000

The previous output shows the summaries for the p-values distribution. First, by chr and next using by pooling.

summary(fullRegsAnno$qvalues)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0047  0.1270  0.3180  0.3120  0.4640  1.0000

This is the numerical summary of the distribution of the q-values.

Un-pooled vs pooled

## Compare them directly
hist(fullRegsAnno$pvalues - fullRegsAnno$pvaluesPool, freq = FALSE, col = "light blue", 
    main = "Difference in p-values", xlab = "p-value - pooled p-value")
lines(density(fullRegsAnno$pvalues - fullRegsAnno$pvaluesPool), col = "red")

plot of chunk difference

This plot shows the paired differences between the by chr p-values and the pooled p-values.

summary(fullRegsAnno$pvalues - fullRegsAnno$pvaluesPool)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## -0.9930  0.0553  0.2800  0.3510  0.6060  1.0000

This is a numerical summary of the differences.

F-stat vs width

p3 <- ggplot(fullRegsAnno, aes(x = log10(L), y = value, colour = chr, alpha = area)) + 
    geom_point() + labs(title = "F-stat vs width (regions)") + xlab("Region width (log10)") + 
    ylab("F-stat") + scale_colour_discrete(limits = chrs)
p4 <- ggplot(fullNullSummary, aes(x = log10(nullwidth), y = nullstat, colour = chr, 
    alpha = area)) + geom_point() + labs(title = "F-stat vs width (null regions)") + 
    xlab("Region width (log10)") + ylab("F-stat") + scale_colour_discrete(limits = chrs)
grid.arrange(p3, p4)

plot of chunk widths

This plot shows the relationship between the F-statistics and the width (length) for the regions. The bottom panel is the same information but this time for the null regions.

Region width and area

p5a <- ggplot(fullRegsAnno, aes(x = log10(L), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of region lengths") + xlab("Region width (log10)") + 
    scale_colour_discrete(limits = chrs)
p5b <- ggplot(subset(fullRegsAnno, pvalues < 0.05), aes(x = log10(L), colour = chr)) + 
    geom_line(stat = "density") + labs(title = "Density of region lengths (significant only)") + 
    xlab("Region width (log10)") + scale_colour_discrete(limits = chrs)
grid.arrange(p5a, p5b)

plot of chunk regLen

This plot shows the density of the region lengths for the regions (all) and regions (significant only).

p7a <- ggplot(fullRegsAnno, aes(x = log10(area), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of region areas") + xlab("Region area (log10)") + 
    scale_colour_discrete(limits = chrs)
p7b <- ggplot(subset(fullRegsAnno, pvalues < 0.05), aes(x = log10(area), colour = chr)) + 
    geom_line(stat = "density") + labs(title = "Density of region areas (significant only)") + 
    xlab("Region area (log10)") + scale_colour_discrete(limits = chrs)
grid.arrange(p7a, p7b)

plot of chunk regArea

This plot shows the density of the region areas for the regions (all) and regions (significant only).

p6 <- ggplot(fullNullSummary, aes(x = log10(nullwidth), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of null region lengths") + xlab("Region width (log10)") + 
    scale_colour_discrete(limits = chrs)
p8 <- ggplot(fullNullSummary, aes(x = log10(area), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of null region areas") + xlab("Region area (log10)") + 
    scale_colour_discrete(limits = chrs)
grid.arrange(p6, p8)

plot of chunk nullLengthArea

This plot shows the density of the null region lengths and areas.

Q-values

Distribution

## Q-values dist
q1 <- ggplot(fullRegsAnno, aes(x = qvalues, colour = chr)) + geom_line(stat = "density") + 
    xlim(0, 1) + labs(title = "Density of q-values") + xlab("q-values") + scale_colour_discrete(limits = chrs)
q1

plot of chunk qvals

This plot shows the distribution of the q-values.

Region width and area, by q-value

q2 <- ggplot(fullRegsAnno, aes(x = log10(L), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of region lengths") + xlab("Region width (log10)") + 
    scale_colour_discrete(limits = chrs)
sub <- subset(fullRegsAnno, qvalues < 0.1)
if (nrow(sub) > 0) {
    q3 <- ggplot(sub, aes(x = log10(L), colour = chr)) + geom_line(stat = "density") + 
        labs(title = "Density of region lengths (qvalue significant only)") + 
        xlab("Region width (log10)") + scale_colour_discrete(limits = chrs)
    grid.arrange(q2, q3)
} else {
    q2
}

plot of chunk regLenQval

This plot shows the density of the region lengths for the regions (all) and regions (qvalue significant only, if any are present!). In this case significance is q-value < 0.10.

q4 <- ggplot(fullRegsAnno, aes(x = log10(area), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of region areas") + xlab("Region area (log10)") + 
    scale_colour_discrete(limits = chrs)
if (nrow(sub) > 0) {
    q5 <- ggplot(subset(fullRegsAnno, qvalues < 0.1), aes(x = log10(area), colour = chr)) + 
        geom_line(stat = "density") + labs(title = "Density of region areas (qvalue significant only)") + 
        xlab("Region area (log10)") + scale_colour_discrete(limits = chrs)
    grid.arrange(q4, q5)
} else {
    q4
}

plot of chunk regAreaQval

rm(sub)

This plot shows the density of the region areas for the regions (all) and regions (qvalue significant only, if any are present!). In this case significance is q-value < 0.10.

Penalization exploration

Below we present two alternative approaches for ranking regions based on the area and penalized by the width of the region.

Scheme 1

p9 <- ggplot(fullRegsAnno, aes(x = log10(penArea1), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of penalized area (scheme 1)") + xlab("Penalized area with sqrt and width > 30 (log10)") + 
    scale_colour_discrete(limits = chrs)
p10 <- ggplot(fullNullSummary, aes(x = log10(penArea1), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of null penalized area (scheme 1)") + xlab("Penalized area with sqrt and width > 30 (log10)") + 
    scale_colour_discrete(limits = chrs)
grid.arrange(p9, p10)

plot of chunk pen1

This plot shows the densities for the penalized areas using:

\[ \begin{cases} Fstat * width \text{ if } width \leq 30 \\ Fstat * 30 + Fstat * \sqrt{ width - 30} & \text{ if } width > 30 \end{cases} \]

Below are the numerical summaries of the penalized areas (scheme 1) for the regions and the null regions in log10 scale.

summary(log10(fullRegsAnno$penArea1))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.32    1.41    1.96    2.04    2.49    3.95
summary(log10(fullNullSummary$penArea1))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.32    1.34    1.65    1.75    2.05    3.13

Scheme 2

p11 <- ggplot(fullRegsAnno, aes(x = log10(penArea2), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of penalized area (scheme 2)") + xlab("Penalized area with log and width > 30 (log10)") + 
    scale_colour_discrete(limits = chrs)
p12 <- ggplot(fullNullSummary, aes(x = log10(penArea2), colour = chr)) + geom_line(stat = "density") + 
    labs(title = "Density of null penalized area (scheme 2)") + xlab("Penalized area with log and width > 30 (log10)") + 
    scale_colour_discrete(limits = chrs)
grid.arrange(p11, p12)

plot of chunk pen2

This plot shows the densities for the penalized areas using:

\[ \begin{cases} Fstat * width \text{ if } width \leq 30 \\ Fstat * 30 + Fstat * \log \left( width - 30 \right) & \text{ if } width > 30 \end{cases} \]

Below is are the numerical summaries of the penalized areas (scheme 2) for the regions and the null regions in log10 scale.

summary(log10(fullRegsAnno$penArea2))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.32    1.41    1.96    2.04    2.49    3.93
summary(log10(fullNullSummary$penArea2))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.32    1.34    1.65    1.75    2.05    3.09

Genomic overview

P-values by chr

plotOverview(regions = fullRegionsDF, type = "pval", base_size = 30, areaRel = 10, 
    legend.position = c(0.97, 0.12))

plot of chunk genomeOverview1

This plot shows the genomic locations of the regions found in the analysis. The significant regions (p-value less than 0.05) are highlighted and the area of the regions is shown on top of each chromosome. Note that the area is in a relative scale.

Q-values

plotOverview(regions = fullRegionsDF, type = "qval", base_size = 30, areaRel = 10, 
    legend.position = c(0.97, 0.12))

plot of chunk genomeOverview2

This plot is similar to the previous one except that significance is now determined by a q-value less than 0.10.

Pooled p-values

## Graphical setup
ann_text <- data.frame(x = 2.25e+08, y = 10, lab = "Area", seqnames = "chrX")
ann_line <- data.frame(x = 2e+08, xend = 2.15e+08, y = 10, seqnames = "chrX")

## Make the plot ^^
autoplot(seqinfo(fullRegionsDF)) + layout_karyogram(fullRegionsDF, aes(fill = significantPool, 
    color = significantPool), geom = "rect") + layout_karyogram(fullRegionsDF, 
    aes(x = midpoint, y = area), geom = "line", color = "coral1", ylim = c(10, 
        20)) + labs(title = "Overview of regions found in the genome; significant: p-value <0.05 (pooled)") + 
    scale_colour_manual(values = c("chartreuse4", "wheat2")) + scale_fill_manual(values = c("chartreuse4", 
    "wheat2")) + geom_text(aes(x = x, y = y), data = ann_text, label = "Area", 
    size = rel(10)) + geom_segment(aes(x = x, xend = xend, y = y, yend = y), 
    data = ann_line, colour = "coral1") + xlab("Genomic coordinate") + theme(text = element_text(size = 30), 
    legend.background = element_blank(), legend.position = c(0.97, 0.12))

plot of chunk genomeOverview3

This is a very similar plot that uses the pooled p-values to determine significance (less than 0.05).

Annotation

plotOverview(regions = fullRegionsDF, annotation = fullRegsAnno, type = "annotation", 
    base_size = 30, areaRel = 10, legend.position = c(0.97, 0.12))

plot of chunk genomeOverview4

This final genomic overview plot shows the annotation region type. Note that the regions are shown only if the information is available. Below is a table of the actual number of results per annotation region type.

table(fullRegsAnno$region, useNA = "always")
## 
##    upstream    promoter overlaps 5'      inside overlaps 3' close to 3' 
##        2816         244          11       29501          13         104 
##  downstream      covers        <NA> 
##        2948           0        6808

Best 20 regions

## Load coverage data
load("../fullCoverage/fullCov.Rdata")

## Graphical setup: ideograms
p.ideos <- lapply(chrs, function(xx) {
    plotIdeogram(genome = "hg19", subchr = xx)
})
names(p.ideos) <- chrs


## Graphical setup: main plotting function
regionPlot <- function(idx, tUse = "pval") {
    ## Chr specific selections
    chr <- as.character(seqnames(fullRegionsDF[idx]))
    chrnum <- gsub("chr", "", chr)
    p.ideo <- p.ideos[[chr]]
    covInfo <- fullCov[[chrnum]]

    ## Make the plot
    p <- plotRegion(idx, regions = fullRegionsDF, annotation = fullRegsAnno, 
        coverageInfo = covInfo, groupInfo = optionsStats$group, titleUse = tUse, 
        txdb = TxDb.Hsapiens.UCSC.hg19.knownGene, p.ideogram = p.ideo)
    print(p)
    rm(p.ideo, covInfo)

    return(invisible(TRUE))
}

By p-value (chr)

Below are the best 20 regions ordered by p-value. There are 10173 regions with p-value less than 0.05 and 7822 that also have a length greater than 10 base-pairs.

bestPval <- head(order(fullRegsAnno$pvalues), 20)
## Genome plots
for (idx in bestPval) {
    regionPlot(idx)
}

plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval plot of chunk bestPval


## Output detail
fullRegsAnno[bestPval, ]
##      chr  start    end value   area cluster indexStart indexEnd   L
## 3   chr1  14524  14568 44.01 1980.6       1        571      615  45
## 7   chr1  14652  14682 39.60 1227.5       1        699      729  31
## 8   chr1  14985  15020 38.47 1384.8       1        892      927  36
## 11  chr1  15834  15905 72.23 5200.7       1        984     1055  72
## 14  chr1  16921  17038 35.63 4203.9       1       1320     1437 118
## 24  chr1  19036  19066 44.15 1368.7       1       2165     2195  31
## 25  chr1  19738  19771 35.28 1199.4       1       2204     2237  34
## 26  chr1  24747  24786 42.61 1704.5       1       6265     6304  40
## 30  chr1 135068 135111 44.28 1948.4       1      10364    10407  44
## 35  chr1 135737 135759 33.28  765.3       1      11033    11055  23
## 36  chr1 135761 135777 33.88  576.0       1      11057    11073  17
## 47  chr1 137660 137679 33.60  671.9       1      12311    12330  20
## 54  chr1 138056 138083 38.50 1078.1       1      12683    12710  28
## 68  chr1 139387 139425 36.93 1440.3       1      13917    13955  39
## 81  chr1 324706 324747 33.67 1414.3       1      21897    21938  42
## 86  chr1 325740 325784 32.62 1467.7       1      22863    22907  45
## 97  chr1 327577 327592 34.01  544.2       1      24107    24122  16
## 98  chr1 327595 327613 34.21  650.0       1      24125    24143  19
## 106 chr1 328243 328284 46.96 1972.2       1      24771    24812  42
## 125 chr1 565244 565288 33.28 1497.5       1      29245    29289  45
##     clusterL  pvalues  qvalues significant significantQval         name
## 3     208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 7     208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 8     208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 11    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 14    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 24    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 25    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 26    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 30    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 35    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 36    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 47    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 54    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 68    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 81    208851 0.001081 0.004675        TRUE            TRUE LOC100133331
## 86    208851 0.001081 0.004675        TRUE            TRUE LOC100133331
## 97    208851 0.001081 0.004675        TRUE            TRUE LOC100132287
## 98    208851 0.001081 0.004675        TRUE            TRUE LOC100132287
## 106   208851 0.001081 0.004675        TRUE            TRUE LOC100132287
## 125   208851 0.001081 0.004675        TRUE            TRUE       OR4F29
##     annotation            description     region distance
## 3    NR_024540                   <NA>       <NA>        0
## 7    NR_024540                   <NA>       <NA>        0
## 8    NR_024540                   <NA>       <NA>        0
## 11   NR_024540                   <NA>       <NA>        0
## 14   NR_024540                   <NA>       <NA>        0
## 24   NR_024540                   <NA>       <NA>        0
## 25   NR_024540                   <NA>       <NA>        0
## 26   NR_024540                   <NA>       <NA>        0
## 30   NR_039983                   <NA>       <NA>        0
## 35   NR_039983                   <NA>       <NA>        0
## 36   NR_039983                   <NA>       <NA>        0
## 47   NR_039983                   <NA>       <NA>        0
## 54   NR_039983                   <NA>       <NA>        0
## 68   NR_039983                   <NA>       <NA>        0
## 81   NR_028327 overlaps exon upstream     inside      418
## 86   NR_028327            inside exon     inside     1452
## 97   NR_028322            inside exon     inside     3685
## 98   NR_028322            inside exon     inside     3703
## 106  NR_028322            inside exon     inside     4351
## 125       <NA>             downstream downstream    56746
##                  subregion insidedistance exonnumber nexons
## 3                     <NA>             NA         NA      7
## 7                     <NA>             NA         NA      7
## 8                     <NA>             NA         NA      7
## 11                    <NA>             NA         NA      5
## 14                    <NA>             NA         NA      7
## 24                    <NA>             NA         NA      7
## 25                    <NA>             NA         NA      7
## 26                    <NA>             NA         NA      7
## 30                    <NA>             NA         NA      5
## 35                    <NA>             NA         NA      5
## 36                    <NA>             NA         NA      5
## 47                    <NA>             NA         NA      5
## 54                    <NA>             NA         NA      5
## 68                    <NA>             NA         NA      5
## 81  overlaps exon upstream              0          3      4
## 86             inside exon              0          4      4
## 97             inside exon              0          3      3
## 98             inside exon              0          3      3
## 106            inside exon              0          3      3
## 125                   <NA>             NA         NA      1
##                             UTR strand geneL codingL chrnum pvaluesPool
## 3                          <NA>      -     0       0      1   0.0001112
## 7                          <NA>      -     0       0      1   0.0001112
## 8                          <NA>      -     0       0      1   0.0001112
## 11                         <NA>      -     0       0      1   0.0001112
## 14                         <NA>      -     0       0      1   0.0001112
## 24                         <NA>      -     0       0      1   0.0001112
## 25                         <NA>      -     0       0      1   0.0001112
## 26                         <NA>      -     0       0      1   0.0001112
## 30                         <NA>      -     0       0      1   0.0001112
## 35                         <NA>      -     0       0      1   0.0001112
## 36                         <NA>      -     0       0      1   0.0001112
## 47                         <NA>      -     0       0      1   0.0001112
## 54                         <NA>      -     0       0      1   0.0001112
## 68                         <NA>      -     0       0      1   0.0001112
## 81  inside transcription region      +  1608    1090      1   0.0001112
## 86                        3'UTR      +  1608    1090      1   0.0001112
## 97                        3'UTR      +  4689    1262      1   0.0001112
## 98                        3'UTR      +  4689    1262      1   0.0001112
## 106                       3'UTR      +  4689    1262      1   0.0001112
## 125                        <NA>      -   938     938      1   0.0001112
##     penArea1 penArea2
## 3     1490.9   1439.6
## 7     1227.5   1187.9
## 8     1248.2   1222.9
## 11    2635.1   2436.9
## 14    1403.0   1228.3
## 24    1368.7   1324.5
## 25    1128.9   1107.2
## 26    1413.1   1376.5
## 30    1494.2   1445.3
## 35     765.3    765.3
## 36     576.0    576.0
## 47     671.9    671.9
## 54    1078.1   1078.1
## 68    1218.7   1189.1
## 81    1126.8   1093.9
## 86    1104.8   1066.8
## 97     544.2    544.2
## 98     650.0    650.0
## 106   1571.4   1525.4
## 125   1127.2   1088.4

By q-value

Below are the best 20 regions ordered by q-value. There are 9185 regions with q-value less than 0.10 and 7193 that also have a length greater than 10 base-pairs.

bestQval <- head(order(fullRegsAnno$qvalues), 20)
## Genome plots
for (idx in bestQval) {
    regionPlot(idx, tUse = "qval")
}

plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval plot of chunk bestQval


## Output detail
fullRegsAnno[bestQval, ]
##      chr  start    end value   area cluster indexStart indexEnd   L
## 3   chr1  14524  14568 44.01 1980.6       1        571      615  45
## 7   chr1  14652  14682 39.60 1227.5       1        699      729  31
## 8   chr1  14985  15020 38.47 1384.8       1        892      927  36
## 11  chr1  15834  15905 72.23 5200.7       1        984     1055  72
## 14  chr1  16921  17038 35.63 4203.9       1       1320     1437 118
## 24  chr1  19036  19066 44.15 1368.7       1       2165     2195  31
## 25  chr1  19738  19771 35.28 1199.4       1       2204     2237  34
## 26  chr1  24747  24786 42.61 1704.5       1       6265     6304  40
## 30  chr1 135068 135111 44.28 1948.4       1      10364    10407  44
## 35  chr1 135737 135759 33.28  765.3       1      11033    11055  23
## 36  chr1 135761 135777 33.88  576.0       1      11057    11073  17
## 47  chr1 137660 137679 33.60  671.9       1      12311    12330  20
## 54  chr1 138056 138083 38.50 1078.1       1      12683    12710  28
## 68  chr1 139387 139425 36.93 1440.3       1      13917    13955  39
## 81  chr1 324706 324747 33.67 1414.3       1      21897    21938  42
## 86  chr1 325740 325784 32.62 1467.7       1      22863    22907  45
## 97  chr1 327577 327592 34.01  544.2       1      24107    24122  16
## 98  chr1 327595 327613 34.21  650.0       1      24125    24143  19
## 106 chr1 328243 328284 46.96 1972.2       1      24771    24812  42
## 125 chr1 565244 565288 33.28 1497.5       1      29245    29289  45
##     clusterL  pvalues  qvalues significant significantQval         name
## 3     208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 7     208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 8     208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 11    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 14    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 24    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 25    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 26    208851 0.001081 0.004675        TRUE            TRUE       WASH7P
## 30    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 35    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 36    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 47    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 54    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 68    208851 0.001081 0.004675        TRUE            TRUE    LOC729737
## 81    208851 0.001081 0.004675        TRUE            TRUE LOC100133331
## 86    208851 0.001081 0.004675        TRUE            TRUE LOC100133331
## 97    208851 0.001081 0.004675        TRUE            TRUE LOC100132287
## 98    208851 0.001081 0.004675        TRUE            TRUE LOC100132287
## 106   208851 0.001081 0.004675        TRUE            TRUE LOC100132287
## 125   208851 0.001081 0.004675        TRUE            TRUE       OR4F29
##     annotation            description     region distance
## 3    NR_024540                   <NA>       <NA>        0
## 7    NR_024540                   <NA>       <NA>        0
## 8    NR_024540                   <NA>       <NA>        0
## 11   NR_024540                   <NA>       <NA>        0
## 14   NR_024540                   <NA>       <NA>        0
## 24   NR_024540                   <NA>       <NA>        0
## 25   NR_024540                   <NA>       <NA>        0
## 26   NR_024540                   <NA>       <NA>        0
## 30   NR_039983                   <NA>       <NA>        0
## 35   NR_039983                   <NA>       <NA>        0
## 36   NR_039983                   <NA>       <NA>        0
## 47   NR_039983                   <NA>       <NA>        0
## 54   NR_039983                   <NA>       <NA>        0
## 68   NR_039983                   <NA>       <NA>        0
## 81   NR_028327 overlaps exon upstream     inside      418
## 86   NR_028327            inside exon     inside     1452
## 97   NR_028322            inside exon     inside     3685
## 98   NR_028322            inside exon     inside     3703
## 106  NR_028322            inside exon     inside     4351
## 125       <NA>             downstream downstream    56746
##                  subregion insidedistance exonnumber nexons
## 3                     <NA>             NA         NA      7
## 7                     <NA>             NA         NA      7
## 8                     <NA>             NA         NA      7
## 11                    <NA>             NA         NA      5
## 14                    <NA>             NA         NA      7
## 24                    <NA>             NA         NA      7
## 25                    <NA>             NA         NA      7
## 26                    <NA>             NA         NA      7
## 30                    <NA>             NA         NA      5
## 35                    <NA>             NA         NA      5
## 36                    <NA>             NA         NA      5
## 47                    <NA>             NA         NA      5
## 54                    <NA>             NA         NA      5
## 68                    <NA>             NA         NA      5
## 81  overlaps exon upstream              0          3      4
## 86             inside exon              0          4      4
## 97             inside exon              0          3      3
## 98             inside exon              0          3      3
## 106            inside exon              0          3      3
## 125                   <NA>             NA         NA      1
##                             UTR strand geneL codingL chrnum pvaluesPool
## 3                          <NA>      -     0       0      1   0.0001112
## 7                          <NA>      -     0       0      1   0.0001112
## 8                          <NA>      -     0       0      1   0.0001112
## 11                         <NA>      -     0       0      1   0.0001112
## 14                         <NA>      -     0       0      1   0.0001112
## 24                         <NA>      -     0       0      1   0.0001112
## 25                         <NA>      -     0       0      1   0.0001112
## 26                         <NA>      -     0       0      1   0.0001112
## 30                         <NA>      -     0       0      1   0.0001112
## 35                         <NA>      -     0       0      1   0.0001112
## 36                         <NA>      -     0       0      1   0.0001112
## 47                         <NA>      -     0       0      1   0.0001112
## 54                         <NA>      -     0       0      1   0.0001112
## 68                         <NA>      -     0       0      1   0.0001112
## 81  inside transcription region      +  1608    1090      1   0.0001112
## 86                        3'UTR      +  1608    1090      1   0.0001112
## 97                        3'UTR      +  4689    1262      1   0.0001112
## 98                        3'UTR      +  4689    1262      1   0.0001112
## 106                       3'UTR      +  4689    1262      1   0.0001112
## 125                        <NA>      -   938     938      1   0.0001112
##     penArea1 penArea2
## 3     1490.9   1439.6
## 7     1227.5   1187.9
## 8     1248.2   1222.9
## 11    2635.1   2436.9
## 14    1403.0   1228.3
## 24    1368.7   1324.5
## 25    1128.9   1107.2
## 26    1413.1   1376.5
## 30    1494.2   1445.3
## 35     765.3    765.3
## 36     576.0    576.0
## 47     671.9    671.9
## 54    1078.1   1078.1
## 68    1218.7   1189.1
## 81    1126.8   1093.9
## 86    1104.8   1066.8
## 97     544.2    544.2
## 98     650.0    650.0
## 106   1571.4   1525.4
## 125   1127.2   1088.4

By p-value (pooled)

Below are the best 20 regions ordered by p-value (pooled ones). There are 42273 regions with pooled p-value less than 0.05 and 11525 that also have a length greater than 10 base-pairs.

bestPooled <- head(order(fullRegsAnno$pvaluesPool), 20)
## Genome plots
for (idx in bestPooled) {
    regionPlot(idx)
}

plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled plot of chunk bestPooled


## Output detail
fullRegsAnno[bestPooled, ]
##     chr start   end value    area cluster indexStart indexEnd   L clusterL
## 1  chr1 14496 14496 21.24   21.24       1        543      543   1   208851
## 2  chr1 14498 14520 27.41  630.54       1        545      567  23   208851
## 3  chr1 14524 14568 44.01 1980.61       1        571      615  45   208851
## 4  chr1 14613 14618 23.65  141.90       1        660      665   6   208851
## 5  chr1 14620 14621 22.88   45.76       1        667      668   2   208851
## 6  chr1 14628 14628 21.16   21.16       1        675      675   1   208851
## 7  chr1 14652 14682 39.60 1227.45       1        699      729  31   208851
## 8  chr1 14985 15020 38.47 1384.78       1        892      927  36   208851
## 9  chr1 15827 15828 23.98   47.97       1        977      978   2   208851
## 10 chr1 15832 15832 21.57   21.57       1        982      982   1   208851
## 11 chr1 15834 15905 72.23 5200.70       1        984     1055  72   208851
## 12 chr1 15908 15914 28.71  200.95       1       1058     1064   7   208851
## 13 chr1 15919 15922 26.07  104.29       1       1069     1072   4   208851
## 14 chr1 16921 17038 35.63 4203.90       1       1320     1437 118   208851
## 15 chr1 17294 17294 21.35   21.35       1       1516     1516   1   208851
## 16 chr1 17636 17637 21.53   43.06       1       1661     1662   2   208851
## 17 chr1 17648 17656 22.29  200.65       1       1673     1681   9   208851
## 18 chr1 17706 17747 31.36 1317.19       1       1731     1772  42   208851
## 19 chr1 17930 17936 23.85  166.94       1       1783     1789   7   208851
## 20 chr1 18281 18284 24.32   97.27       1       1928     1931   4   208851
##     pvalues  qvalues significant significantQval   name annotation
## 1  0.762162 0.397218       FALSE           FALSE WASH7P  NR_024540
## 2  0.032432 0.069893        TRUE            TRUE WASH7P  NR_024540
## 3  0.001081 0.004675        TRUE            TRUE WASH7P  NR_024540
## 4  0.202162 0.205469       FALSE           FALSE WASH7P  NR_024540
## 5  0.314595 0.265980       FALSE           FALSE WASH7P  NR_024540
## 6  0.795676 0.408462       FALSE           FALSE WASH7P  NR_024540
## 7  0.001081 0.004675        TRUE            TRUE WASH7P  NR_024540
## 8  0.001081 0.004675        TRUE            TRUE WASH7P  NR_024540
## 9  0.160000 0.177187       FALSE           FALSE WASH7P  NR_024540
## 10 0.642162 0.373408       FALSE           FALSE WASH7P  NR_024540
## 11 0.001081 0.004675        TRUE            TRUE WASH7P  NR_024540
## 12 0.015135 0.040782        TRUE            TRUE WASH7P  NR_024540
## 13 0.061622 0.104660       FALSE           FALSE WASH7P  NR_024540
## 14 0.001081 0.004675        TRUE            TRUE WASH7P  NR_024540
## 15 0.722162 0.391637       FALSE           FALSE WASH7P  NR_024540
## 16 0.648649 0.373408       FALSE           FALSE WASH7P  NR_024540
## 17 0.422703 0.305249       FALSE           FALSE WASH7P  NR_024540
## 18 0.003243 0.011858        TRUE            TRUE WASH7P  NR_024540
## 19 0.177297 0.189232       FALSE           FALSE WASH7P  NR_024540
## 20 0.141622 0.168419       FALSE           FALSE WASH7P  NR_024540
##    description region distance subregion insidedistance exonnumber nexons
## 1         <NA>   <NA>        0      <NA>             NA         NA      7
## 2         <NA>   <NA>        0      <NA>             NA         NA      7
## 3         <NA>   <NA>        0      <NA>             NA         NA      7
## 4         <NA>   <NA>        0      <NA>             NA         NA      7
## 5         <NA>   <NA>        0      <NA>             NA         NA      7
## 6         <NA>   <NA>        0      <NA>             NA         NA      7
## 7         <NA>   <NA>        0      <NA>             NA         NA      7
## 8         <NA>   <NA>        0      <NA>             NA         NA      7
## 9         <NA>   <NA>        0      <NA>             NA         NA      5
## 10        <NA>   <NA>        0      <NA>             NA         NA      5
## 11        <NA>   <NA>        0      <NA>             NA         NA      5
## 12        <NA>   <NA>        0      <NA>             NA         NA      5
## 13        <NA>   <NA>        0      <NA>             NA         NA      5
## 14        <NA>   <NA>        0      <NA>             NA         NA      7
## 15        <NA>   <NA>        0      <NA>             NA         NA      4
## 16        <NA>   <NA>        0      <NA>             NA         NA      7
## 17        <NA>   <NA>        0      <NA>             NA         NA      7
## 18        <NA>   <NA>        0      <NA>             NA         NA      7
## 19        <NA>   <NA>        0      <NA>             NA         NA      7
## 20        <NA>   <NA>        0      <NA>             NA         NA      7
##     UTR strand geneL codingL chrnum pvaluesPool penArea1 penArea2
## 1  <NA>      -     0       0      1   0.0001112    21.24    21.24
## 2  <NA>      -     0       0      1   0.0001112   630.54   630.54
## 3  <NA>      -     0       0      1   0.0001112  1490.87  1439.60
## 4  <NA>      -     0       0      1   0.0001112   141.90   141.90
## 5  <NA>      -     0       0      1   0.0001112    45.76    45.76
## 6  <NA>      -     0       0      1   0.0001112    21.16    21.16
## 7  <NA>      -     0       0      1   0.0001112  1227.45  1187.86
## 8  <NA>      -     0       0      1   0.0001112  1248.21  1222.91
## 9  <NA>      -     0       0      1   0.0001112    47.97    47.97
## 10 <NA>      -     0       0      1   0.0001112    21.57    21.57
## 11 <NA>      -     0       0      1   0.0001112  2635.07  2436.94
## 12 <NA>      -     0       0      1   0.0001112   200.95   200.95
## 13 <NA>      -     0       0      1   0.0001112   104.29   104.29
## 14 <NA>      -     0       0      1   0.0001112  1402.99  1228.30
## 15 <NA>      -     0       0      1   0.0001112    21.35    21.35
## 16 <NA>      -     0       0      1   0.0001112    43.06    43.06
## 17 <NA>      -     0       0      1   0.0001112   200.65   200.65
## 18 <NA>      -     0       0      1   0.0001112  1049.49  1018.78
## 19 <NA>      -     0       0      1   0.0001112   166.94   166.94
## 20 <NA>      -     0       0      1   0.0001112    97.27    97.27

By area

Below are the best 20 regions ordered by area.

bestArea <- head(order(fullRegsAnno$area, decreasing = TRUE), 20)
## Genome plots
for (idx in bestArea) {
    regionPlot(idx)
}

plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea plot of chunk bestArea


## Output detail
fullRegsAnno[bestArea, ]
##         chr     start       end  value  area cluster indexStart indexEnd
## 41217  chrX  73065732  73066652  72.16 66462      23     930786   931706
## 41144  chrX  73046528  73047108  93.62 54392      23     925212   925792
## 41208  chrX  73064868  73065420  85.09 47057      23     929947   930499
## 41171  chrX  73062356  73062878  62.27 32567      23     927741   928263
## 41157  chrX  73048956  73049275  66.57 21303      23     926401   926720
## 41287  chrX  73071466  73071782  63.78 20217      23     934813   935129
## 25909 chr14 106203213 106203756  33.66 18313      18    1576583  1577126
## 28988 chr16  22547417  22547711  59.31 17496       1     985283   985577
## 41138  chrX  73045850  73046143  57.42 16881      23     924534   924827
## 41063  chrX  73041843  73042153  49.20 15303      23     921481   921791
## 16255  chr8  57014691  57014750 241.65 14499       9     764313   764372
## 11848  chr6   5973899   5973953 255.45 14050       1      92967    93021
## 12335  chr6  31324267  31324427  87.06 14017       3     593808   593968
## 26238 chr14 106405612 106406098  28.11 13689      18    1633317  1633803
## 29301 chr16  29392794  29392970  71.06 12577       1    1224459  1224635
## 26063 chr14 106323037 106323474  28.54 12500      18    1603520  1603957
## 41290  chrX  73072026  73072218  64.48 12445      23     935198   935390
## 41158  chrX  73053073  73053251  66.88 11972      23     926777   926955
## 24432 chr13  68406742  68406793 230.18 11969      14     562098   562149
## 25908 chr14 106202855 106203208  33.64 11910      18    1576225  1576578
##         L clusterL  pvalues qvalues significant significantQval
## 41217 921   170283 0.003521 0.01477        TRUE            TRUE
## 41144 581   170283 0.003521 0.01477        TRUE            TRUE
## 41208 553   170283 0.003521 0.01477        TRUE            TRUE
## 41171 523   170283 0.003521 0.01477        TRUE            TRUE
## 41157 320   170283 0.003521 0.01477        TRUE            TRUE
## 41287 317   170283 0.003521 0.01477        TRUE            TRUE
## 25909 544   298055 0.010959 0.08090        TRUE            TRUE
## 28988 295  1577510 0.003077 0.01962        TRUE            TRUE
## 41138 294   170283 0.003521 0.01477        TRUE            TRUE
## 41063 311   170283 0.003521 0.01477        TRUE            TRUE
## 16255  60    80903 0.003597 0.01894        TRUE            TRUE
## 11848  55   189132 0.002398 0.02654        TRUE            TRUE
## 12335 161   729134 0.002398 0.02654        TRUE            TRUE
## 26238 487   298055 0.035616 0.12191        TRUE           FALSE
## 29301 177  1577510 0.003077 0.01962        TRUE            TRUE
## 26063 438   298055 0.032877 0.12191        TRUE           FALSE
## 41290 193   170283 0.003521 0.01477        TRUE            TRUE
## 41158 179   170283 0.003521 0.01477        TRUE            TRUE
## 24432  52     2579 0.009174 0.03720        TRUE            TRUE
## 25908 354   298055 0.010959 0.08090        TRUE            TRUE
##               name annotation   description     region distance
## 41217         XIST  NR_001564          <NA>       <NA>        0
## 41144         TSIX  NR_003255          <NA>       <NA>        0
## 41208         XIST  NR_001564          <NA>       <NA>        0
## 41171         XIST  NR_001564          <NA>       <NA>        0
## 41157         TSIX  NR_003255          <NA>       <NA>        0
## 41287         XIST  NR_001564          <NA>       <NA>        0
## 25909     KIAA0125  NM_001786      upstream   upstream   152224
## 28988 LOC100132247       <NA>   inside exon     inside     4812
## 41138         TSIX  NR_003255          <NA>       <NA>        0
## 41063         TSIX  NR_003255          <NA>       <NA>        0
## 16255          MOS  NP_001663    downstream downstream    11791
## 11848         NRN1       <NA>          <NA>       <NA>        0
## 12335        HLA-B       <NA> inside intron     inside      307
## 26238     KIAA0125  NM_001786          <NA>       <NA>        0
## 29301      SNX29P2  NR_002939          <NA>       <NA>        0
## 26063     KIAA0125  NM_001786      upstream   upstream    32506
## 41290         XIST  NR_001564          <NA>       <NA>        0
## 41158         XIST  NR_001564          <NA>       <NA>        0
## 24432        PCDH9       <NA>      upstream   upstream   602274
## 25908     KIAA0125  NM_001786      upstream   upstream   152772
##           subregion insidedistance exonnumber nexons
## 41217          <NA>             NA         NA      6
## 41144          <NA>             NA         NA      1
## 41208          <NA>             NA         NA      6
## 41171          <NA>             NA         NA      6
## 41157          <NA>             NA         NA      1
## 41287          <NA>             NA         NA      6
## 25909          <NA>             NA         NA      4
## 28988   inside exon              0          4      4
## 41138          <NA>             NA         NA      1
## 41063          <NA>             NA         NA      1
## 16255          <NA>             NA         NA      1
## 11848          <NA>             NA         NA      3
## 12335 inside intron            -38          1      2
## 26238          <NA>             NA         NA      6
## 29301          <NA>             NA         NA      7
## 26063          <NA>             NA         NA      4
## 41290          <NA>             NA         NA      6
## 41158          <NA>             NA         NA      6
## 24432          <NA>             NA         NA      5
## 25908          <NA>             NA         NA      4
##                               UTR strand  geneL codingL chrnum pvaluesPool
## 41217                        <NA>      -      0       0      X   0.0001112
## 41144                        <NA>      +      0       0      X   0.0001112
## 41208                        <NA>      -      0       0      X   0.0001112
## 41171                        <NA>      -      0       0      X   0.0001112
## 41157                        <NA>      +      0       0      X   0.0001112
## 41287                        <NA>      -      0       0      X   0.0001112
## 25909                        <NA>      +  32550    1546     14   0.0001112
## 28988              overlaps 3'UTR      +   5236    4321     16   0.0001112
## 41138                        <NA>      +      0       0      X   0.0001112
## 41063                        <NA>      +      0       0      X   0.0001112
## 16255                        <NA>      -   1040    1040      8   0.0001112
## 11848                        <NA>      -      0       0      6   0.0001112
## 12335 inside transcription region      -   1435     871      6   0.0001112
## 26238                        <NA>      +      0       0     14   0.0001112
## 29301                        <NA>      +      0       0     16   0.0001112
## 26063                        <NA>      +  32550    1546     14   0.0001112
## 41290                        <NA>      -      0       0      X   0.0001112
## 41158                        <NA>      -      0       0      X   0.0001112
## 24432                        <NA>      - 927502  923785     13   0.0001112
## 25908                        <NA>      +  32550    1546     14   0.0001112
##       penArea1 penArea2
## 41217     4319     2655
## 41144     5006     3399
## 41208     4499     3085
## 41171     3251     2254
## 41157     3131     2375
## 41287     2994     2274
## 25909     1773     1220
## 28988     2745     2110
## 41138     2655     2043
## 41063     2301     1754
## 16255     8573     8071
## 11848     8941     8486
## 12335     3608     3036
## 26238     1444     1015
## 29301     2993     2486
## 26063     1433     1028
## 41290     2758     2263
## 41158     2823     2341
## 24432     7985     7617
## 25908     1615     1204

By area (scheme 1)

Below are the best 20 regions ordered by penalized area (scheme 1).

bestPen1Area <- head(order(fullRegsAnno$penArea1, decreasing = TRUE), 20)
## Genome plots
for (idx in bestPen1Area) {
    regionPlot(idx)
}

plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area plot of chunk bestPen1Area


## Output detail
fullRegsAnno[bestPen1Area, ]
##         chr     start       end value  area cluster indexStart indexEnd  L
## 11848  chr6   5973899   5973953 255.5 14050       1      92967    93021 55
## 16255  chr8  57014691  57014750 241.6 14499       9     764313   764372 60
## 24432 chr13  68406742  68406793 230.2 11969      14     562098   562149 52
## 24418 chr13  61211942  61211988 220.2 10348       8     548916   548962 47
## 19718 chr10 126556165 126556216 209.1 10875      20    1895727  1895778 52
## 8472   chr3 183833902 183833952 180.2  9189      53    2466186  2466236 51
## 16302  chr8  70899787  70899844 166.0  9629      12     899205   899262 58
## 2197   chr1 115079805 115079858 160.1  8644      28    2598156  2598209 54
## 37532 chr20  21095583  21095643 154.4  9418       5     325744   325804 61
## 4746   chr2  61644256  61644310 155.9  8573      21     885719   885773 55
## 3474   chr1 201947862 201947913 156.8  8155      48    4134040  4134091 52
## 3429   chr1 187532975 187533029 154.8  8514      41    4006009  4006063 55
## 34164 chr18  18699328  18699381 152.0  8206       4     225839   225892 54
## 16537  chr8 102381423 102381469 155.3  7301      21    1219137  1219183 47
## 3415   chr1 185196775 185196827 151.1  8008      41    3985494  3985546 53
## 14430  chr7  63602128  63602187 147.7  8862      12     908720   908779 60
## 27207 chr15  79155773  79155827 149.4  8218       9    1301187  1301241 55
## 13014  chr6  56736096  56736160 145.3  9444      11    1364846  1364910 65
## 3039   chr1 158495473 158495525 149.3  7914      37    3451365  3451417 53
## 7269   chr3  48162537  48162582 150.5  6922      15     731985   732030 46
##       clusterL  pvalues  qvalues significant significantQval    name
## 11848   189132 0.002398 0.026542        TRUE            TRUE    NRN1
## 16255    80903 0.003597 0.018939        TRUE            TRUE     MOS
## 24432     2579 0.009174 0.037202        TRUE            TRUE   PCDH9
## 24418    18195 0.009174 0.037202        TRUE            TRUE   TDRD3
## 19718    42428 0.003367 0.034869        TRUE            TRUE FAM175B
## 8472    485196 0.002141 0.026714        TRUE            TRUE   HTR3E
## 16302    88048 0.003597 0.018939        TRUE            TRUE  PRDM14
## 2197     85499 0.001081 0.004675        TRUE            TRUE DENND2C
## 37532    98566 0.004444 0.030002        TRUE            TRUE  PLK1S1
## 4746    153251 0.002053 0.015686        TRUE            TRUE   USP34
## 3474    195658 0.001081 0.004675        TRUE            TRUE   RNPEP
## 3429    185407 0.001081 0.004675        TRUE            TRUE PLA2G4A
## 34164    79962 0.006711 0.016171        TRUE            TRUE   ROCK1
## 16537   206310 0.003597 0.018939        TRUE            TRUE  NACAP1
## 3415    185407 0.001081 0.004675        TRUE            TRUE    SWT1
## 14430   554568 0.001208 0.006552        TRUE            TRUE  ZNF727
## 27207    44548 0.002778 0.025589        TRUE            TRUE MORF4L1
## 13014    88851 0.002398 0.026542        TRUE            TRUE     DST
## 3039    710950 0.001081 0.004675        TRUE            TRUE   OR6Y1
## 7269    638180 0.002141 0.026714        TRUE            TRUE    MAP4
##         annotation   description     region distance     subregion
## 11848         <NA>          <NA>       <NA>        0          <NA>
## 16255    NP_001663    downstream downstream    11791          <NA>
## 24432         <NA>      upstream   upstream   602274          <NA>
## 24418         <NA>    downstream downstream   241351          <NA>
## 19718 NM_001164318    downstream downstream    65811          <NA>
## 8472          <NA>    downstream downstream    19050          <NA>
## 16302 NP_001034662    downstream downstream    83718          <NA>
## 2197          <NA>          <NA>       <NA>        0          <NA>
## 37532         <NA>      upstream   upstream    10981          <NA>
## 4746          <NA> inside intron     inside    53539 inside intron
## 3474  NM_001024808      upstream   upstream     3853          <NA>
## 3429          <NA>    downstream downstream   734943          <NA>
## 34164         <NA>      upstream   upstream     7516          <NA>
## 16537         <NA>   inside exon     inside      302   inside exon
## 3415          <NA> inside intron     inside    70484 inside intron
## 14430         <NA>    downstream downstream    96307          <NA>
## 27207         <NA>      upstream   upstream     9345          <NA>
## 13014         <NA>      upstream   upstream    19382          <NA>
## 3039          <NA>    downstream downstream    22370          <NA>
## 7269          <NA>      upstream   upstream    31768          <NA>
##       insidedistance exonnumber nexons                         UTR strand
## 11848             NA         NA      3                        <NA>      -
## 16255             NA         NA      1                        <NA>      -
## 24432             NA         NA      5                        <NA>      -
## 24418             NA         NA     14                        <NA>      +
## 19718             NA         NA      9                        <NA>      +
## 8472              NA         NA      9                        <NA>      +
## 16302             NA         NA      8                        <NA>      -
## 2197              NA         NA     30                        <NA>      -
## 37532             NA         NA     14                        <NA>      +
## 4746           -3571          2     80 inside transcription region      -
## 3474              NA         NA     11                        <NA>      +
## 3429              NA         NA     18                        <NA>      +
## 34164             NA         NA     33                        <NA>      -
## 16537              0          1      1 inside transcription region      +
## 3415            3882         16     19 inside transcription region      +
## 14430             NA         NA      4                        <NA>      +
## 27207             NA         NA      6                        <NA>      +
## 13014             NA         NA     34                        <NA>      -
## 3039              NA         NA      1                        <NA>      -
## 7269              NA         NA     19                        <NA>      -
##        geneL codingL chrnum pvaluesPool penArea1 penArea2
## 11848      0       0      6   0.0001112     8941     8486
## 16255   1040    1040      8   0.0001112     8573     8071
## 24432 927502  923785     13   0.0001112     7985     7617
## 24418 177422  107174     13   0.0001112     7513     7229
## 19718  34885   33141     10   0.0001112     7255     6920
## 8472    9931    9163      3   0.0001112     6231     5954
## 16302  19676   17783      8   0.0001112     5859     5534
## 2197       0       0      1   0.0001112     5586     5311
## 37532 120634  120457     20   0.0001112     5492     5162
## 4746  283259  282590      2   0.0001112     5455     5178
## 3474   23509   23049      1   0.0001112     5440     5189
## 3429  160081  134153      1   0.0001112     5418     5143
## 34164 162109  159526     18   0.0001112     5303     5042
## 16537    702     407      8   0.0001112     5301     5101
## 3415  134622  129961      1   0.0001112     5258     5007
## 14430  33106   32927      7   0.0001112     5240     4933
## 27207  18875   18112     15   0.0001112     5230     4964
## 13014 237560  237434      6   0.0001112     5218     4875
## 3039     977     977      1   0.0001112     5196     4948
## 7269  238589  145828      3   0.0001112     5116     4932

By area (scheme 2)

Below are the best 20 regions ordered by penalized area (scheme 2).

bestPen2Area <- head(order(fullRegsAnno$penArea2, decreasing = TRUE), 20)
## Genome plots
for (idx in bestPen2Area) {
    regionPlot(idx)
}

plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area plot of chunk bestPen2Area


## Output detail
fullRegsAnno[bestPen2Area, ]
##         chr     start       end value  area cluster indexStart indexEnd  L
## 11848  chr6   5973899   5973953 255.5 14050       1      92967    93021 55
## 16255  chr8  57014691  57014750 241.6 14499       9     764313   764372 60
## 24432 chr13  68406742  68406793 230.2 11969      14     562098   562149 52
## 24418 chr13  61211942  61211988 220.2 10348       8     548916   548962 47
## 19718 chr10 126556165 126556216 209.1 10875      20    1895727  1895778 52
## 8472   chr3 183833902 183833952 180.2  9189      53    2466186  2466236 51
## 16302  chr8  70899787  70899844 166.0  9629      12     899205   899262 58
## 2197   chr1 115079805 115079858 160.1  8644      28    2598156  2598209 54
## 3474   chr1 201947862 201947913 156.8  8155      48    4134040  4134091 52
## 4746   chr2  61644256  61644310 155.9  8573      21     885719   885773 55
## 37532 chr20  21095583  21095643 154.4  9418       5     325744   325804 61
## 3429   chr1 187532975 187533029 154.8  8514      41    4006009  4006063 55
## 16537  chr8 102381423 102381469 155.3  7301      21    1219137  1219183 47
## 34164 chr18  18699328  18699381 152.0  8206       4     225839   225892 54
## 3415   chr1 185196775 185196827 151.1  8008      41    3985494  3985546 53
## 27207 chr15  79155773  79155827 149.4  8218       9    1301187  1301241 55
## 3039   chr1 158495473 158495525 149.3  7914      37    3451365  3451417 53
## 14430  chr7  63602128  63602187 147.7  8862      12     908720   908779 60
## 7269   chr3  48162537  48162582 150.5  6922      15     731985   732030 46
## 13014  chr6  56736096  56736160 145.3  9444      11    1364846  1364910 65
##       clusterL  pvalues  qvalues significant significantQval    name
## 11848   189132 0.002398 0.026542        TRUE            TRUE    NRN1
## 16255    80903 0.003597 0.018939        TRUE            TRUE     MOS
## 24432     2579 0.009174 0.037202        TRUE            TRUE   PCDH9
## 24418    18195 0.009174 0.037202        TRUE            TRUE   TDRD3
## 19718    42428 0.003367 0.034869        TRUE            TRUE FAM175B
## 8472    485196 0.002141 0.026714        TRUE            TRUE   HTR3E
## 16302    88048 0.003597 0.018939        TRUE            TRUE  PRDM14
## 2197     85499 0.001081 0.004675        TRUE            TRUE DENND2C
## 3474    195658 0.001081 0.004675        TRUE            TRUE   RNPEP
## 4746    153251 0.002053 0.015686        TRUE            TRUE   USP34
## 37532    98566 0.004444 0.030002        TRUE            TRUE  PLK1S1
## 3429    185407 0.001081 0.004675        TRUE            TRUE PLA2G4A
## 16537   206310 0.003597 0.018939        TRUE            TRUE  NACAP1
## 34164    79962 0.006711 0.016171        TRUE            TRUE   ROCK1
## 3415    185407 0.001081 0.004675        TRUE            TRUE    SWT1
## 27207    44548 0.002778 0.025589        TRUE            TRUE MORF4L1
## 3039    710950 0.001081 0.004675        TRUE            TRUE   OR6Y1
## 14430   554568 0.001208 0.006552        TRUE            TRUE  ZNF727
## 7269    638180 0.002141 0.026714        TRUE            TRUE    MAP4
## 13014    88851 0.002398 0.026542        TRUE            TRUE     DST
##         annotation   description     region distance     subregion
## 11848         <NA>          <NA>       <NA>        0          <NA>
## 16255    NP_001663    downstream downstream    11791          <NA>
## 24432         <NA>      upstream   upstream   602274          <NA>
## 24418         <NA>    downstream downstream   241351          <NA>
## 19718 NM_001164318    downstream downstream    65811          <NA>
## 8472          <NA>    downstream downstream    19050          <NA>
## 16302 NP_001034662    downstream downstream    83718          <NA>
## 2197          <NA>          <NA>       <NA>        0          <NA>
## 3474  NM_001024808      upstream   upstream     3853          <NA>
## 4746          <NA> inside intron     inside    53539 inside intron
## 37532         <NA>      upstream   upstream    10981          <NA>
## 3429          <NA>    downstream downstream   734943          <NA>
## 16537         <NA>   inside exon     inside      302   inside exon
## 34164         <NA>      upstream   upstream     7516          <NA>
## 3415          <NA> inside intron     inside    70484 inside intron
## 27207         <NA>      upstream   upstream     9345          <NA>
## 3039          <NA>    downstream downstream    22370          <NA>
## 14430         <NA>    downstream downstream    96307          <NA>
## 7269          <NA>      upstream   upstream    31768          <NA>
## 13014         <NA>      upstream   upstream    19382          <NA>
##       insidedistance exonnumber nexons                         UTR strand
## 11848             NA         NA      3                        <NA>      -
## 16255             NA         NA      1                        <NA>      -
## 24432             NA         NA      5                        <NA>      -
## 24418             NA         NA     14                        <NA>      +
## 19718             NA         NA      9                        <NA>      +
## 8472              NA         NA      9                        <NA>      +
## 16302             NA         NA      8                        <NA>      -
## 2197              NA         NA     30                        <NA>      -
## 3474              NA         NA     11                        <NA>      +
## 4746           -3571          2     80 inside transcription region      -
## 37532             NA         NA     14                        <NA>      +
## 3429              NA         NA     18                        <NA>      +
## 16537              0          1      1 inside transcription region      +
## 34164             NA         NA     33                        <NA>      -
## 3415            3882         16     19 inside transcription region      +
## 27207             NA         NA      6                        <NA>      +
## 3039              NA         NA      1                        <NA>      -
## 14430             NA         NA      4                        <NA>      +
## 7269              NA         NA     19                        <NA>      -
## 13014             NA         NA     34                        <NA>      -
##        geneL codingL chrnum pvaluesPool penArea1 penArea2
## 11848      0       0      6   0.0001112     8941     8486
## 16255   1040    1040      8   0.0001112     8573     8071
## 24432 927502  923785     13   0.0001112     7985     7617
## 24418 177422  107174     13   0.0001112     7513     7229
## 19718  34885   33141     10   0.0001112     7255     6920
## 8472    9931    9163      3   0.0001112     6231     5954
## 16302  19676   17783      8   0.0001112     5859     5534
## 2197       0       0      1   0.0001112     5586     5311
## 3474   23509   23049      1   0.0001112     5440     5189
## 4746  283259  282590      2   0.0001112     5455     5178
## 37532 120634  120457     20   0.0001112     5492     5162
## 3429  160081  134153      1   0.0001112     5418     5143
## 16537    702     407      8   0.0001112     5301     5101
## 34164 162109  159526     18   0.0001112     5303     5042
## 3415  134622  129961      1   0.0001112     5258     5007
## 27207  18875   18112     15   0.0001112     5230     4964
## 3039     977     977      1   0.0001112     5196     4948
## 14430  33106   32927      7   0.0001112     5240     4933
## 7269  238589  145828      3   0.0001112     5116     4932
## 13014 237560  237434      6   0.0001112     5218     4875

Wallclock time

## Process the time info
time <- lapply(fullTime, function(x) data.frame(diff(x)))
time <- do.call(rbind, time)
colnames(time) <- "sec"
time$sec <- as.integer(round(time$sec))
time$min <- time$sec/60
time$chr <- paste0("chr", gsub("\\..*", "", rownames(time)))
time$step <- gsub(".*\\.", "", rownames(time))
rownames(time) <- seq_len(nrow(time))

## Make plot
ggplot(time, aes(x = step, y = min, colour = chr)) + geom_point() + labs(title = "Wallclock time by step") + 
    scale_colour_discrete(limits = chrs) + scale_x_discrete(limits = names(fullTime[[1]])[-1]) + 
    ylab("Time (min)") + xlab("Step")

plot of chunk time

This plot shows the wallclock time spent in each of the derfinder2 analysis steps.

Permutations info

Below is the information on how the samples were permutted when performing the permutations.

## Get the permutation information
nSamples <- seq_len(length(optionsStats$group))
seeds <- 1:10
permuteInfo <- lapply(seeds, function(x) {
    set.seed(x)
    idx <- sample(nSamples)
    data.frame(optionsStats$group[idx])
})
permuteInfo <- cbind(data.frame(optionsStats$group), do.call(cbind, permuteInfo))
colnames(permuteInfo) <- c("original", paste0("perm", 1:10))
## The raw information permuteInfo

n <- names(table(permuteInfo[, 2]))
permuteDetail <- data.frame(matrix(NA, nrow = 10 * length(n), ncol = 2 + length(n)))
permuteDetail[, 1] <- rep(1:10, each = length(n))
permuteDetail[, 2] <- rep(n, 10)
colnames(permuteDetail) <- c("permutation", "group", as.character(n))
l <- 1
m <- 3:ncol(permuteDetail)
for (j in n) {
    k <- which(permuteInfo[, 1] == j)
    for (i in 2:11) {
        permuteDetail[l, m] <- table(permuteInfo[k, i])
        l <- l + 1
    }
}
permuteDetail
##    permutation group CEU YRI
## 1            1   CEU  16   5
## 2            1   YRI  15   6
## 3            2   CEU  15   6
## 4            2   YRI  14   7
## 5            3   CEU  14   7
## 6            3   YRI  12   9
## 7            4   CEU  14   7
## 8            4   YRI  14   7
## 9            5   CEU  14   7
## 10           5   YRI  15   6
## 11           6   CEU   5   5
## 12           6   YRI   6   4
## 13           7   CEU   6   4
## 14           7   YRI   7   3
## 15           8   CEU   7   3
## 16           8   YRI   9   1
## 17           9   CEU   7   3
## 18           9   YRI   7   3
## 19          10   CEU   7   3
## 20          10   YRI   6   4

This table shows how the group labels were permutted. This can be useful to detect whether a permutation in particular had too many samples of a group labeled as another group, meaning that the resulting permutted group label resulted in pretty much a name change.

summary(permuteDetail[, m])
##       CEU            YRI   
##  Min.   : 5.0   Min.   :1  
##  1st Qu.: 7.0   1st Qu.:3  
##  Median :10.5   Median :5  
##  Mean   :10.5   Mean   :5  
##  3rd Qu.:14.0   3rd Qu.:7  
##  Max.   :16.0   Max.   :9

This table shows the summary per group of the first table and can be used for faster detection of anomalies.

## By index
permuteInfoIdx <- lapply(seeds, function(x) {
    set.seed(x)
    idx <- sample(nSamples)
    data.frame(nSamples[idx])
})
permuteInfoIdx <- cbind(data.frame(nSamples), do.call(cbind, permuteInfoIdx))
colnames(permuteInfoIdx) <- c("original", paste0("perm", 1:10))

## Only if you really want to see the indexes
permuteInfoIdx

Note that in derfinder2 the re-sampling of the samples is done without replacement. This is done in an effort to avoid singular model matrices. While the sample balance is the same across the permutations, what changes are the adjusted variables (including the column medians).

Reproducibility

## Save the hard work
if (!procFiles) {
    save(fullRegsAnno, file = paste0(prefix, "/fullRegsAnnoPooled.Rdata"))
    save(fullNullSummary, file = paste0(prefix, "/fullNullSummary.Rdata"))
}

## Date the report was generated
Sys.time()
## [1] "2013-08-15 23:24:00 EDT"
## Processing time in seconds
proc.time()
##    user  system elapsed 
##  4196.9   162.8  5038.5
## Session info
sessionInfo()
## R version 3.0.1 Patched (2013-05-16 r62754)
## Platform: x86_64-unknown-linux-gnu (64-bit)
## 
## locale:
##  [1] LC_CTYPE=en_US.iso885915       LC_NUMERIC=C                  
##  [3] LC_TIME=en_US.iso885915        LC_COLLATE=en_US.iso885915    
##  [5] LC_MONETARY=en_US.iso885915    LC_MESSAGES=en_US.iso885915   
##  [7] LC_PAPER=C                     LC_NAME=C                     
##  [9] LC_ADDRESS=C                   LC_TELEPHONE=C                
## [11] LC_MEASUREMENT=en_US.iso885915 LC_IDENTIFICATION=C           
## 
## attached base packages:
## [1] grid      parallel  methods   stats     graphics  grDevices utils    
## [8] datasets  base     
## 
## other attached packages:
##  [1] rtracklayer_1.20.2                     
##  [2] Cairo_1.5-2                            
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_2.9.0
##  [4] GenomicFeatures_1.12.1                 
##  [5] AnnotationDbi_1.22.5                   
##  [6] Biobase_2.20.0                         
##  [7] ggbio_1.8.5                            
##  [8] GenomicRanges_1.12.3                   
##  [9] gridExtra_0.9.1                        
## [10] ggplot2_0.9.3.1                        
## [11] IRanges_1.18.1                         
## [12] BiocGenerics_0.6.0                     
## [13] derfinder2_0.0.13                      
## [14] RcppArmadillo_0.3.820                  
## [15] Rcpp_0.10.4                            
## [16] knitrBootstrap_0.7.0                   
## [17] markdown_0.6.3                         
## [18] knitr_1.2                              
## 
## loaded via a namespace (and not attached):
##  [1] biomaRt_2.16.0          Biostrings_2.28.0      
##  [3] biovizBase_1.8.0        bitops_1.0-5           
##  [5] BSgenome_1.28.0         bumphunter_1.1.11      
##  [7] cluster_1.14.4          codetools_0.2-8        
##  [9] colorspace_1.2-2        DBI_0.2-7              
## [11] dichromat_2.0-0         digest_0.6.3           
## [13] doRNG_1.5.3             evaluate_0.4.3         
## [15] foreach_1.4.0           formatR_0.7            
## [17] gtable_0.1.2            Hmisc_3.10-1.1         
## [19] iterators_1.0.6         itertools_0.1-1        
## [21] labeling_0.1            lattice_0.20-15        
## [23] MASS_7.3-26             matrixStats_0.8.1      
## [25] munsell_0.4             plyr_1.8               
## [27] proto_0.3-10            qvalue_1.34.0          
## [29] RColorBrewer_1.0-5      RCurl_1.95-4.1         
## [31] reshape2_1.2.2          R.methodsS3_1.4.2      
## [33] Rsamtools_1.12.3        RSQLite_0.11.3         
## [35] scales_0.2.3            stats4_3.0.1           
## [37] stringr_0.6.2           tcltk_3.0.1            
## [39] tools_3.0.1             VariantAnnotation_1.6.5
## [41] XML_3.96-1.1            zlibbioc_1.6.0

This report written by L. Collado Torres and was generated using knitrBootstrap.

citation("derfinder2")

To cite package 'derfinder2' in publications use:

Leonardo Collado-Torres, Alyssa Frazee, Andrew Jaffe and Jeffrey Leek (2013). derfinder2: Fast differential expression analysis of RNA-seq data at base-pair resolution. R package version 0.0.13. https://github.com/lcolladotor/derfinder2

A BibTeX entry for LaTeX users is

@Manual{, title = {derfinder2: Fast differential expression analysis of RNA-seq data at base-pair resolution}, author = {Leonardo Collado-Torres and Alyssa Frazee and Andrew Jaffe and Jeffrey Leek}, year = {2013}, note = {R package version 0.0.13}, url = {https://github.com/lcolladotor/derfinder2}, }